A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus

Martin Henriksson, Ramandeep Jindal, Catarina Sternhufvud (), Klas Bergenheim, Elisabeth Sörstadius and Michael Willis
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Martin Henriksson: PAREXEL International
Ramandeep Jindal: PAREXEL International
Catarina Sternhufvud: AstraZeneca
Klas Bergenheim: AstraZeneca
Elisabeth Sörstadius: AstraZeneca
Michael Willis: The Swedish Institute for Health Economics, IHE

PharmacoEconomics, 2016, vol. 34, issue 6, No 4, 569-585

Abstract: Abstract Background Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM. Objectives This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM. Methods A systematic review of Embase®, MEDLINE®, MEDLINE® In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities. Results We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis. Conclusions Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of ‘best in class’ methods for the different technical aspects of T1DM modelling.

Date: 2016
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DOI: 10.1007/s40273-015-0374-8

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